Deciphering patterns of human genetic variation will prove integral in our understanding of the genetic underpinnings of disease and human evolution. Currently, human population genetic data is being produced at an incredible rate; however the methods for its analysis are in their infancy. My research aims to create computational and statistical methods for the analysis of population genetic data at the genome-scale, in an evolutionary context. In particular this proposal focuses on uncovering those regions of the human genome that may have been subject to recent selection. Beyond describing those genetic changes which make us uniquely human, identifying the targets of recent selection may prove to play an important role in medicine, for example by pointing to targets for resistance to infectious agents. There are two specific aims to the proposal: 1) the development of a coalescent-based likelihood method for detecting patterns of polymorphism and divergence that are incompatible with neutral evolution and its application to human variation data and divergence data from the chimp and soon-to-be-sequenced macaque genomes, and 2) the extension of Hidden Markov Models (HMMs) to population genetic inference. [unreadable] [unreadable]